When does AI-mediated feedback support EFL/ESL learning? A mixed-methods systematic review and meta-analysis

Abstract

Artificial-intelligence tools now deliver feedback to English language learners through automated writing evaluation, speech-recognition systems, intelligent tutoring, and generative chatbots, but evidence on their effects remains uneven. This convergent mixed-methods systematic review synthesised 195 studies of AI-mediated feedback in English as a foreign or second language published between 2023 and 2026, combining random-effects meta-analysis of comparative performance effects with thematic synthesis of learner-uptake evidence. Seventeen studies contributed an eligible comparative effect on objective language performance. The pooled estimate was large and highly heterogeneous, Hedges’ g = 1.19, 95% CI [0.57, 1.81], τ² = 1.42, I² = 93.2%, with a 95% prediction interval from −1.34 to 3.72. Restricting the pool to effects below g = 2 produced a more stable model, g = 0.62 [0.40, 0.85], k = 13, I² = 52.6%. Domain estimates were unstable: writing, g = 1.36 [0.26, 2.46], k = 9, carried I² = 95.7%, while speaking and grammar confidence intervals included zero. Thematic synthesis of study-level summaries identified noticing, uptake and revision, scaffolding and practice, and affective response as recurring proximal mechanisms. All fitted models had positive point estimates; this descriptive pattern does not resolve bias, comparator differences, or uncertainty across settings. The estimate’s magnitude depends heavily on which effects are admitted, and the corpus rarely records whether outcomes were assessed with the AI tool available, so improvement in an assisted product cannot be separated from change in independent performance. Every contributing effect has been traced to a named outcome and contrast in its primary report, and the eight records whose extraction was corrected during revision are logged with their source basis in the supplement.